Viruses pose a significant threat to human health based on their ability to cause diseases such as AIDS, influenza, hepatitis, cancer and the common cold. Basic research has revealed much about the molecular details of processes that are essential for viruses to reproduce, but a critical barrier to further progress is a lack of understanding of how factors of the host environment quantitatively impact virus growth. The proposed research aims to address this barrier by advancing new quantitative experiments and computational models of virus growth. The approach will employ, combine and extend perspectives drawn from the fields of experimental evolution and systems biology. Specific aims of the proposed project will be to: (1) advance computational models of virus growth to provide an integrated dynamic measure of cellular resource use, (2) quantify how resource use is altered during evolutionary adaptation of virus to host cells, and (3) elucidate mechanisms by which viral adaptation alters resource use. The methodology established by this research will set a basis for advancing the development of data-driven predictive models of virus growth. Results of the research will have the potential to significantly impact several fields: the design anti-viral therapeutics, applications of viruses in oncolytic (anti-tumor) cancer therapies, and development of new vaccines. At a more basic level, predictive models of virus growth will be useful for better understanding how viruses grow, evolve and persist in Nature.